Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8033
comunitat-uji-handle3:10234/8636
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental StressFecha de publicación
2019Editor
MDPIISSN
1424-8220Cita bibliográfica
Cipresso, Pietro; Colombo, Desirée; Riva, Giuseppe. "Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress." Sensors, 2019, vol. 19, núm. 4, p.781Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/1424-8220/19/4/781Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The goal of this study was to provide reliable quantitative analyses of psycho-physiological measures during acute mental stress. Acute, time-limited stressors are used extensively as experimental stimuli in psychop ... [+]
The goal of this study was to provide reliable quantitative analyses of psycho-physiological measures during acute mental stress. Acute, time-limited stressors are used extensively as experimental stimuli in psychophysiological research. In particular, the Stroop Color Word Task and the Arithmetical Task have been widely used in several settings as effective mental stressors. We collected psychophysiological data on blood volume pulse, thoracic respiration, and skin conductance from 60 participants at rest and during stressful situations. Subsequently, we used statistical univariate tests and multivariate computational approaches to conduct comprehensive studies on the discriminative properties of each condition in relation to psychophysiological correlates. The results showed evidence of a greater discrimination capability of the Arithmetical Task compared to the Stroop test. The best predictors were the short time Heart Rate Variability (HRV) indices, in particular, the Respiratory Sinus Arrhythmia index, which in turn could be predicted by other HRV and respiratory indices in a hierarchical, multi-level regression analysis. Thus, computational psychometrics analyses proved to be an effective tool for studying such complex variables. They could represent the first step in developing complex platforms for the automatic detection of mental stress, which could improve the treatment. [-]
Publicado en
Sensors, 2019, vol. 19, núm. 4, p.781Derechos de acceso
info:eu-repo/semantics/openAccess
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- PSB_Articles [1321]
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